Machine learning is a multidisciplinary discipline, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in the study of how computers simulate or realize human learning behavior to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve its own performance.
It is the core of artificial intelligence and the fundamental way to make computers intelligent.
Even if we don’t plan on going into algorithms, it’s not a bad thing to know a little about the basics of machine learning. Here’s a list of books that will get you started on the basics of machine learning.
List of machine learning books
Illustrated machine learning
“Graphical machine learning” with rich graphics, starting from the minimum square method, based on the minimum square method to achieve a variety of machine learning algorithms are introduced in detail. The first part introduces the general situation of machine learning field. Part ⅱ and Part ⅲ introduce various supervised regression algorithms and classification algorithms respectively. Part ⅳ introduces various supervised learning algorithms. Part ⅴ introduces emerging algorithms in machine learning. Most algorithms in the book have corresponding MATLAB program source code, which can be used for simple testing.
Author’s brief introduction
Jiji Sugiyama was born in Osaka in 1974. He graduated from Tokyo Institute of Technology with a PhD in computer engineering, and is currently a professor at The University of Tokyo and a visiting professor at the National Institute of Informatics, Japan. Mainly engaged in theoretical research and algorithm development of machine learning, as well as applications in signal and image processing. In 2011, he was awarded the Special Memorial Award by The President of Information Processing Society of Japan. The author of “statistical machine learning, DensityRatioEstimationinMachineLearning, etc. At the same time PatternRecognitionandMachineLearning one of Japan’s version of the translator.
Yongwei Xu, PhD candidate at University of Tokyo in 2009, is currently working as a postdoctoral fellow at Institute of Spatial Information Science, University of Tokyo. His research interest covers pattern recognition and machine learning, image processing and computer vision, and he is interested in data mining, big data and information architecture.
Python machine learning
Machine learning is rapidly changing our world. Almost every day we read about how machine learning is changing everyday life. If you buy goods on e-commerce sites like Taobao or JD.com, or watch shows on video sites like IQiyi or Tencent Video, or even do a Baidu search, you’ve already touched the application of machine learning.
Users of these services generate data, which is collected, preprocessed and used to train the model, which uses this data to provide a better user experience. In addition, there are many products or services using machine learning technology that will soon be popularized in our lives, such as hands-free driverless cars, smart smart home products, and considerate shopping robots. Now is an ideal time to get into the application development of machine learning.
This book covers the necessary knowledge in machine learning fields such as supervised learning, unsupervised learning, model optimization and natural language processing, and attaches great importance to the practicability and operability of knowledge from the content structure. The book is taught step by step and follows and respects the cognitive rules of beginners on machine learning knowledge. This book is suitable for those who have a certain programming language and algorithm foundation.
Author’s brief introduction
Duan Xiaoxiao, founder of Junxi Technology, graduated from Peking University. More than 10 years of project management experience in domestic first-line Internet/e-commerce companies. Its cross-border e-commerce projects have been supported by special policies such as “E-commerce Demonstration Project of National Development and Reform Commission”, “Zhongguancun Modern Service Industry Pilot Project”, “Beijing Information Infrastructure Upgrading Project” and “Beijing Foreign Trade Comprehensive Public Platform”. His current research focuses on machine learning and deep learning.
100 sides machine Learning: Algorithm engineers take you to an interview
The field of artificial intelligence is evolving faster than anyone can imagine, and we are fortunate to have written this book before AI takes over the world.
There are more than 100 interview questions and solutions for machine learning algorithm engineers, most of them from real-life Hulu algorithm-research jobs. Book from daily work and life in all kinds of interesting phenomenon, not only includes the basic knowledge of machine learning, but also includes algorithms to become outstanding engineer related skills, more important is condensed the author for a heart of enthusiasm in the field of artificial intelligence, aims to develop the reader to find and solve problems and expand the question ability, establish the love of machine learning, Draw the grand blueprint of the ai world together.
Starting from classic machine learning fields such as feature engineering, model evaluation and dimension reduction, this book will build a knowledge system that algorithm engineers must prepare. See the progress of neural network, reinforcement learning, generative adversarial network and other new scientific research, know the rise and fall of the field of deep learning; In the last chapter, the readers will be shown the application of artificial intelligence in the Yin collar era.
Author’s brief introduction
Chu Ge Yue is Hulu’s vice President of global research and development and general manager of its Research and development Center in China. He was the co-founder and CEO of Landscape Mobile, formerly the product director of Yahoo’s Beijing Global R&D Center, project General Manager of Microsoft’s Beijing R&D Center, and senior Software Architect of Yahoo USA. Ge Yue received his MASTER’s degree and doctor’s degree in computer science from Stanford University and his master’s degree in applied mathematics from Stony Brook University of New York. He studied in the Department of Computer Science and Technology at Tsinghua University. His research results have won many patents, and in 2005, he was awarded the z-best paper of the decade by the Database Committee of the Computer Society of America.
Hulu Kids, 15 talents from Hulu’s Beijing Innovation Lab. They make use of their expertise in machine learning, deep learning and other fields and algorithm models to establish a set of customized machine AI platforms, changing the recommendation engine, video coding and decoding, content understanding, advertising and other online business technologies closely related to users.
Machine learning
Machine learning is an important branch of computer science and artificial intelligence. As an introductory textbook in this field, this book covers all aspects of machine learning fundamentals as far as possible. The book consists of 16 chapters, which are roughly divided into three parts: Part 1 (Chapters 1-3) introduces the basic knowledge of machine learning;
Part 2 (Chapters 4-10) discusses some classical and commonly used machine learning methods (decision tree, neural network, support vector machine, Bayesian classifier, ensemble learning, clustering, dimensionality reduction and metric learning).
The third part (chapters 11 ~ 16) is advanced knowledge, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probability graph model, rule learning and reinforcement learning, etc. Each chapter is accompanied by problem sets and related reading materials for further exploration by interested readers.
The book can be used as a textbook for undergraduate or graduate students majoring in computer, automation and related fields in universities and colleges, as well as for researchers and engineers interested in machine learning.
About the author:
Zhou Zhihua, Professor, Department of Computer Science, Nanjing University, ACM Distinguished Scientist, IEEE Fellow, IAPR Fellow, IET/IEE Fellow, Member of China Computer Society.
Recipient of national Science Fund for Distinguished Young Scholars, Distinguished Professor of Changjiang Scholars program. He has served as executive editor, associate editor, associate editor and editorial board member of various SCI(E) journals. Director of Artificial Intelligence and Pattern Recognition Committee, Chinese Society for Computer Science, Director of Machine Learning Committee, Chinese Society for Artificial Intelligence, vice Chairman of Data Mining Technical Committee, IEEE Society for Computational Intelligence.
